Browsing by Author "Tatoglu, E."
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ArticlePublication Metadata only Entrepreneurial orientation, CEO power and firm performance: An upper echelons theory perspective(Emerald, 2023-05-22) Saiyed, Abrarali Mohammadusmanali; Tatoglu, E.; Ali, S.; Dutta, D. K.; Entrepreneurship; SAIYED, Abraralı MohammadusmanalıPurpose: Adopting insights from the upper echelons theory, this study aims to investigate the relationship between entrepreneurial orientation (EO) and firm performance under the contingent influence of chief executive officer (CEO) power. Design/methodology/approach: Data were collected from a sample of large publicly-traded Indian software firms using the Prowess Database of Center for Monitoring Indian Economy (CMIE). Panel data regression analysis was used to test the study's hypotheses. Findings: The results indicate that EO has an inverted U-shaped relation with firm performance. Strong support is also found for a negative moderating influence of CEO power on the inverted U-shaped relationship between EO and firm financial performance, suggesting that powerful CEOs eventually harm entrepreneurial firms. Practical implications: The study encourages firms to have entrepreneurship orientation, but at a moderate level, to get the maximum benefit of EO. The study also explains to managers to what extent CEO power drives EO. Originality/value: The study contributes to the intersection of corporate entrepreneurship and upper echelons theory. The study shows that CEO power negatively affects the EO and firm's performance relationship. This study holds important insights for managers of entrepreneurial firms, especially in international contexts and emerging markets.ArticlePublication Metadata only Using machine learning tools for forecasting natural gas consumption in the province of Istanbul(Elsevier, 2019-05) Beyca, Ö. F.; Ervural, B. C.; Tatoglu, E.; Özuyar, Pınar Gökçin; Zaim, S.; Entrepreneurship; ÖZUYAR, PinarCommensurate with unprecedented increases in energy demand, a well-constructed forecasting model is vital to managing energy policies effectively by providing energy diversity and energy requirements that adapt to the dynamic structure of the country. In this study, we employ three alternative popular machine learning tools for rigorous projection of natural gas consumption in the province of Istanbul, Turkey's largest natural gas-consuming mega-city. These tools include multiple linear regression (MLR), an artificial neural network approach (ANN) and support vector regression (SVR). The results indicate that the SVR is much superior to ANN technique, providing more reliable and accurate results in terms of lower prediction errors for time series forecasting of natural gas consumption. This study could well serve a useful benchmarking study for many emerging countries due to the data structure, consumption frequency, and consumption behavior of consumers in various time-periods.